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  • Logical abilities  (2)
  • 9M Temporary seismic network (MULTIPLE), Mugello Basin  (1)
  • 2020-2024  (3)
  • 2020-2023
  • 1970-1974
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  • 2020-2024  (3)
  • 2020-2023
  • 1970-1974
Year
  • 1
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    A temporary network for monitoring seismicity in the Mugello basin (Northern Apennines, Italy) (2023)
    INGV
    Details
    Publication Date: 2023-03-07
    Description: Within the geodynamic context of the Northern Apennines (Italy), one of the most relevant seismogenic areas is the Mugello basin (NorthEastern Tuscany). The area has a welldocumented record of seismicity; the two major historical earthquakes occurred in 1542 (Mw=6.0) and in 1919 (Mw=6.4). The proximity of the Mugello Basin to densely urbanized areas and the potential impact of strong earthquakes on the cultural heritage in the nearby (~30km) city of Florence makes a better knowledge of the seismicity in that area an important target. Following this argument, by mid2019 we deployed 9 temporary stations within and around the Mugello basin, complementing the permanent monitoring networks currently operating in the area. This report describes the details of the temporary network deployment and shows sample recordings and locations from a Mw=4.5 earthquake which struck the NW margin of the basin on Dec. 9, 2019.
    Description: Published
    Description: 1-40
    Description: 4T. Sismicità dell'Italia
    Description: N/A or not JCR
    Keywords: 9M Temporary seismic network (MULTIPLE), Mugello Basin
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
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    Chapter A structural equation model to measure logical competences (2022)
    Firenze University Press
    Details
    Publication Date: 2022-06-02
    Description: Logical abilities are a ubiquitous ingredient in all those contexts that take into account soft skills, argumentative skills or critical thinking. However, the relationship between logical models and the enhancement of these abilities is rarely explicitly considered. Two aspects of the issue are particularly critical in our opinion, namely: (i) the lack of statistically relevant data concerning these competences; (ii) the absence of reliable indices that might be used to detect and measure the possession of abilities underlying the aforementioned skills. This paper addresses both aspects of this topic by presenting the results of a research that we conducted between October and December 2020 on students enrolled in various degree courses at the University of Florence. The dataset has been collected by a three-stage initiative. We started from an entrance examination to assess the students' initial abilities. This test included ten questions, each regarding a specific aspect of logical reasoning. Then, students underwent a short training course to strengthen their knowledge of logic. At the end of the short course, students underwent a final examination to evaluate the effectiveness of the training. To verify the effectiveness of the training program we used students’ characteristics into a Structural Equation Model comparing the distribution of abilities before and after the training.
    Keywords: SEM ; Logical abilities ; Training course effectiveness
    Language: English
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  • 3
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    Chapter A structural equation model to measure logical competences (2022)
    Firenze University Press
    Details
    Publication Date: 2022-09-22
    Description: Logical abilities are a ubiquitous ingredient in all those contexts that take into account soft skills, argumentative skills or critical thinking. However, the relationship between logical models and the enhancement of these abilities is rarely explicitly considered. Two aspects of the issue are particularly critical in our opinion, namely: (i) the lack of statistically relevant data concerning these competences; (ii) the absence of reliable indices that might be used to detect and measure the possession of abilities underlying the aforementioned skills. This paper addresses both aspects of this topic by presenting the results of a research that we conducted between October and December 2020 on students enrolled in various degree courses at the University of Florence. The dataset has been collected by a three-stage initiative. We started from an entrance examination to assess the students' initial abilities. This test included ten questions, each regarding a specific aspect of logical reasoning. Then, students underwent a short training course to strengthen their knowledge of logic. At the end of the short course, students underwent a final examination to evaluate the effectiveness of the training. To verify the effectiveness of the training program we used students’ characteristics into a Structural Equation Model comparing the distribution of abilities before and after the training.
    Keywords: SEM ; Logical abilities ; Training course effectiveness ; bic Book Industry Communication::J Society & social sciences::JH Sociology & anthropology::JHB Sociology::JHBC Social research & statistics
    Language: English
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